1. Field of the Invention
The present invention relates to a method for rapidly establishing the 3D space relation of captured images. More particularly, the present invention relates to a method for rapidly building an image space relation using plane filtering constraint.
2. Description of the Related Art
Three-dimensional information reconstruction technique is one of the important research areas in the computer vision field. Through this technique, the computer can automatically determine the three-dimensional information of environmental objects through an image extraction system for subsequent processing and applications.
At present, the most common three-dimensional reconstruction technique includes using a DV camera to get a series of continuous images taken from different shooting angles such that each image contains a number of feature points. One major aspect of the three-dimensional reconstruction technique is to perform a matching operation of the consecutive images and look for their corresponding feature points so that a suitable projective geometry relation can be established between the images.
Let {tilde over (p)}1 and {tilde over (p)}2 be two feature points randomly selected from the two consecutive images taken at different angles. If these two feature points are projected from the same coordinate in three-dimensional (3D) space, then the two feature points satisfy the following formula:
                              p          ~                1        T            ⁢              F                  3          ⨯          3                    ⁢                        p          ~                2              =    0    ,            where      ⁢                          ⁢                        p          ~                1              =          [                                                  u              1                                                                          v              1                                                            1                              ]        ,            and      ⁢                          ⁢                        p          ~                2              =          [                                                  u              2                                                                          v              2                                                            1                              ]        ,where {tilde over (p)}1 and {tilde over (p)}2 are two feature points and F3×3 is a 3×3 fundamental matrix representing the epipolar geometry formed by the two images.
Accordingly, the epipolar geometry, represented by a 3×3 fundamental matrix, connects the spatial relation between the camera intrinsic and extrinsic parameters and the 3D point coordinate of two captured pictures. Hence, it is for finding whether two feature points correspond to each other or not. The conventional method of finding the fundamental matrix is shown in FIG. 1. First, in step S101, eight matched pairs of feature points obtained by comparing samples are randomly selected from a set of continuous image data taken at different shooting angles. In step S102, the eight groups of feature points are used to compute the fundamental matrix. Then, in step S103, the error value of each selected feature point to an epipolar line is calculated to obtain a plurality of epipolar errors. Then, in step S105, the epipolar error values are sorted in order to find a median value in the error value list.
Step S107 determines if a computed value is equal to a preset value or not. If the computed value is not equal to the preset value, in step S109, the computed value is incremented by one and the steps starting from S101 are repeated. On the other hand, if the computed value is equal to the preset value, step S111 is carried out, that is, to find out the smallest median error by arranging each computed median error in a computation in order. Finally, step S113 is executed to calculate the epipolar geometry fundamental matrix using the smallest median error.
Although the conventional technique can accurately compute the values in the epipolar geometry matrix, the fundamental matrix computation for the corresponding feature points and the computation of the epipolar error value for each computed fundamental matrix require a lot of time. In the meantime, if the system demands to reconstruct a more accurate 3D mode, more times of fundamental matrix estimation from the median errors are required to compare and find the most suitable median error. Ultimately, system computation will be slowed down.